Joe Biagini burst onto the scene in 2016 as a Rule 5 pick by the Toronto Blue Jays. His mid-90s fastball, and devastating hook, took the league by storm, and his numbers in his first year were dominant. Besides all this, his personality and quirkiness really made the headlines, from awkward post-game interviews to missing high-fives from Jimmy Fallon at The Tonight Show.

Regardless of all the off-field attention Biagini has received, his play on the field has been spectacular. He kept the Blue Jays bullpen together, when they were in absolute peril early in the 2016 season, before they got Jason Grilli and Joaquin Benoit. Now, after putting up a 3.06 ERA and striking out 62 over 67.2 innings a season ago, while only allowing three home runs, is there an opportunity for Joe to transition to being a starter?

Pitch Types

A year ago, Biagini was pretty much a fastball/curveball type pitcher, throwing his fastball 60.2% of the time, his slider 15.3% of the time, and his curveball 17.4% of the time, while occasionally throwing in a changeup. This is quite standard for a reliever who is only there to pitch an inning, and even more common for a pitcher who has not risen above Double-A. With just three pitches, being a starter and going through the lineup more than once would be challenging without getting hit hard. His fastball velocity averaged 94.2 mph, with his curveball at 80.1 mph, and with a harder slider at 89.4 mph. His changeup started getting more developed toward the end of the season and touches 86.1 mph, but it was rarely used. Pitch values showed his fastball was 2.9 runs above average, due to its deceptiveness, the slider was 0.5 above average, the curveball was 1.4 below average, and the changeup was 0.4 below average.

2017 Joe Biagini has mixed up his pitches more and is now throwing his fastball 56.5% of the time. He started throwing a cutter this season and throws it about 9.4% of the time, and it is in the low-90s range. He also throws the slider less — only 8.2% of the time — and throws his 12-6 overhand curveball 15.7% of the time. And now he owns a solid changeup, which he throws 10.2% of the time. The array of pitches at his disposal now is quite interesting, as he has more weapons to attack hitters and keep them off-balance. Biagini improved all of his secondary pitches over the offseason and pitch values have the evidence. His fastball has decreased to only being 0.5 runs above average, which is understandable seeing as this is now his second season and hitters are more accustomed to the pitch, and also it’s early May. However, his slider has risen from being barely above average at 0.5 to quite above average at 2.2, the curveball went from being below average to 1.2 above average, and his changeup now is also above average at 0.3.

The improvement of his secondary pitches this season, and the addition of a hard cutter, have made him an elite reliever and possible starter. His velocity is still there on his fastball, his spin rate is above average, and his pitches are starting to get nastier. His new repertoire has made him efficient and aggressive, and these are strong indicators of a starting pitcher. Although the Blue Jays do have one of the best rotations in the league and it would be tough for Joe to crack it, there have been some injuries to their starters, with J.A. Happ and Aaron Sanchez both going down with injuries. With the Mat Latos experiment presumably finished (hopefully), the Jays could and should stretch Biagini out a little and see what he can do as a starter.

His groundball rate is over 58% this season, his K/9 is 8.2, and he does not walk many batters. His fastball velocity on average has increased from 94.3 mph to 94.9 mph and his slider has gone from 89.3 mph to 91.2 mph. With the increase in velocity, and the higher usage of his changeup, success is imminent.

History of Starting

Before coming over to the Blue Jays in the Rule 5 Draft, Biagini was a starter in the Giants system. He made 22 starts in 2015 for the ‘AA’ Richmond Flying Squirrels, and made 23 starts in 2014 for the ‘High A’ San Jose Giants. In those seasons he amassed 130.1 IP and 128.0 IP respectively while putting up good numbers, with a 4.o1 ERA in 2014 and a 2.42 ERA in 2015. Even as a starter, he had high ground-ball rates and lower fly-ball rates, but his strikeout numbers were lower, most likely due to the fact he did not have the same repertoire as he has today with the Blue Jays. There is a small but definite track record for Biagini as a starter, and there seems to be a necessity for the Blue Jays to use him in that role temporarily.

Joe is a big, strong guy who pounds the strike zone, has a full array of pitches at his disposal, and can locate his offspeed pitches well. Stretching him out could be the next card the Blue Jays use and it might actually be a game-changer.

I often read articles that say strikeouts are bad if you don’t have power. Inspired by the success of K%-BB% with pitchers I tried to do something similar with hitters to generate a stat that gets predictable with a smaller sample size than wRC+ due to the elimination of BABIP. This could be useful for prospect analysis or also early-season stats.

The rationale basically was to take something bad (Ks) and subtract good things (ISO, BB). To do this, I first scaled BB and K from percent to decimal dividing it by 100. So instead of 22% Ks I would use .22 to get it to the same scale as ISO. You could also scale ISO to percent, but it does not really matter.

Doing that, I found out that most good hitters were below zero. I looked at players that had at least 1000 PA from 2014 to April 2017.

Here is the complete document.

The worst K-BB-ISO was a positive .133 by Chris Johnson (75 wRC+) while the best was a negative .256 by David Ortiz. The average was a negative .05. The 25th percentile was negative .012 (Rajai Davis, 95 wRC+) while the 75th percentile was a negative .09 (Charlie Blackmon, 110 wRC+). Based on this, I conclude that good values are something like negative .1 or better, while values that approach zero are bad and positive values are atrocious.

Overall, the Pearson Coefficient between wRC+ and K-BB-ISO was a negative .75. A negative correlation is expected because the good values are below zero, and the correlation is significant.

The top 20 in K-BB-ISO all have a wRC+ above or equal to 120 and are ranked in the top 50 in wRC+. In the bottom 20 there are three hitters with a wRC+ slightly above 100 but most are near the bottom of the leaderboard.

Now BABIP is not random and there is a skill that is related to contact quality, but then again ISO is also related to contact quality — the guys who hit the ball hard and at decent angles usually also have good ISOs, while the put-everything-in-play-weakly guys usually have bad ISOs (and often bad BB%).

So here is what I look at in a prospect:

excellent: <-0.15 (expect 120 or better wRC+)

good: -0.08 to -0.14 (expect 105 to 120 wRC+)

OK: -0.03 to -0.07 (expect 90 to 105 wRC+)

red flag: above -0.03

Now there is a disclaimer to this: The K-BB-ISO might underrate ground-ball-heavy hard hitters who have lower ISOs but generally solid contact quality. For example, Christian Yelich is just 146th out of 246 in K-BB-ISO over that time frame but 45th in wRC+. It might also overrate fly-ball-heavy pull hitters with high pop-up rates. Examples of this are Brian Dozier (68th wRC+, 29th K-BB-ISO) or Jose Bautista (17th wRC+, 3rd K-BB-ISO).

Also you have to consider park and league factors as there are some very hitter-friendly leagues and parks in the minors (for example the PCL) and HR/FB luck also needs to be considered.

But overall, the leaderboards look quite similar and K-BB-ISO might be a good indicator for success if you want to eliminate BABIP from the equation. Basically this is pretty simple — if you don’t walk or slug a lot, you better not strike out. And if you strike out a lot, you better have something to make up for it.

My analytical background is not the best, though, and maybe somebody who has a little more skill in that field could look at the data and see if I’m onto something.

Andrew Benintendi is just 22 years old and has already shown that he belongs with some of Major League Baseball’s better hitters. He has just 195 career plate appearances, but he’s hitting a very impressive .297/.366/.448 so far in his young career. That’s a batting line that is 21% better than league average (121 wRC+). His patience, combined with his ability to put the bat on the ball, leads one to believe that this is sustainable.

So far in 2017, Benintendi has a walk rate of 9.1% and a strikeout rate of just 13%. His O-Swing% and overall Swing% are both above average at 27% and 42.5%, respectively. He also rarely swings and misses. Of 190 qualified hitters in 2017, Benintendi has the 18^th lowest swinging-strike rate, at 5.5%. I think it’s safe to say that even at such a young age, Benintendi has a good idea of where the strike zone is. His numbers in 2017 are very good, with a line of .299/.377/.403, but they could probably be much better if he wasn’t so heavily penalized for the “lefty strike.”

So far this year, 135 left-handed hitters have seen at least 100 pitches. According to Baseball Savant, of those 135, Benintendi has seen the most called strikes that were off the plate away, with 22. To visualize, here is a strike-zone plot that includes every single called strike on Benintendi this year.

As you can see, a large amount of called strikes on Benintendi have either been up and away, away, or down and away.

Most of these calls have come in an 0-0 count as well. In the same sample of left-handed hitters, Benintendi has seen the most 0-0 called strikes that were off the outside part of the plate, with 12. An astounding 4.1% of all the pitches he has seen this season have been 0-0 called strikes that were off the outer part of the plate, which also leads that 135-player sample. There is only one other player above 4% and just 10 other players above 3%. Here is a visual of the called strikes Benintendi has seen with an 0-0 count.

Benintendi has been put into an 0-1 hole on pitches that are off the plate away more often than any other left-handed hitter that has seen 100 or more pitches. Starting off an at-bat with an 0-1 count is much different than starting with a 1-0 count. It’s only April 23, but this could be something to keep an eye on moving forward. If these strike calls begin to even out, and Benintendi has more calls go his way, his already impressive numbers may start to look even more impressive.

You may have heard that the Reds are approaching their bullpen a bit differently than other teams this season. The Reds aren’t expected to be particularly good this season, and as such, they are a bit more free to experiment.

One recent game highlighted the new-school approach of manager Bryan Price. In the third inning of Monday’s game, Brandon Finnegan started the 3rd inning with a 5-run lead and proceeded to implode, loading the bases before walking the first run of the game in. With no outs recorded yet in the inning, Finnegan was primed to give up several more. Price pulled the trigger on a highly unusual move: He went to the bullpen in favor of Michael Lorenzen, one of his better bullpen arms.

This decision was lauded by quite a few writers and pundits, including those here at FanGraphs. Craig Edwards used it as the impetus for examining the overall usage in the Reds bullpen so far this year, and Ben Lindbergh and Jeff Sullivan called it out in their latest “Effectively Wild” episode. The emphasis, in both cases, was on the decision to bring Lorenzen into the game. Which was a great decision! It was weird! It was wonderful! Most importantly, it worked!

There’s another aspect of this Lorenzen appearance that shouldn’t go overlooked, though. After Lorenzen worked the 3rd inning with great success, he stayed on for the 4th inning, in which he maintained their 5-1 lead. He retired the side in order with 10 pitches in the 4th, having thrown 14 in the 3rd. The Reds tacked on another run in the top of the 5th, and Price stuck with Lorenzen again for the bottom of the inning, now with a 5-run cushion. Lorenzen, once again, set down the side in order, this time on just 8 pitches. With 32 total pitches on the day, Price elected to turn to a lesser pair of arms in Cody Reed and Wandy Peralta to finish out the game (although not before allowing Lorenzen to lead off the top of the 6th at the plate).

While the 3rd inning represented a quintessential high-leverage situation, the 4th contained much less leverage, and the 5th, still less. The numbers bear this out: In the third inning, Lorenzen faced three batters in situations commanding a Leverage Index of 2.68, 2.66, and 2.53. The total Leverage Index of these three batters was a whopping 7.87. By contrast, the total LI associated with Lorenzen’s work in the 4th and 5th innings was 2.40. The six outs Lorenzen got in those innings weren’t as important, cumulatively, as the least important hitter in the 3rd inning!

(Click the graph for an interactive version)

Price was rightfully lauded for bringing one of his best pitchers into one of the most critical moments of the game. That’s only half of the equation, though. Knowing when to take a key reliever out of the game, in the context of the season as a whole, is just as important as knowing when to put him in.

As Edwards rightly notes, Andrew Miller is only on pace for about 88 innings this season. Andrew Miller threw 74.1 IP last season, and it was the most he had ever thrown in his tenure as a full-time reliever. It’s not as though the Yankees or Indians were trying to limit his usage — it’s that a reliever, any reliever, has a limit to the number of innings (and more appropriately, the number of pitches) they can throw in a season without breaking down or losing their effectiveness.

The question, then, is how to maximize the value of these innings. Lorenzen threw 2 innings and 18 pitches that he, quite possibly, didn’t need to throw. He consumed 2.40 “units” of leverage in the process. The next day, he was (quite predictably) unavailable. Price, faced with a close/late game situation, had to throw Peralta in the 7th inning of a one-run game, where he retired the top of the Pirates’ lineup in order, but consumed 4.22 “units” of leverage — 75% more than Lorenzen did in those two innings the day before.

This isn’t to say that “perfect” bullpen usage is achievable. The nature of the game is to guess when the situation you’re faced with will be the most important in the remainder of the game, or the remainder of the series, or the remainder of the homestand. In some cases, a more important, later, closer, more tense situation will arise in the same game, and you’ll have used your most effective bullets. In other cases, you’ll have used a pitcher in a big spot one day, and he’ll be unavailable in an even bigger spot the next day. In still others, the team will go on a run of 4-5 close games in a row, and lesser parts are needed to fill the surplus of close/late innings.

(Click the graph for an interactive version)

But the concept of “perfect” bullpen usage must start with the recognition of constraints, and an approach that optimizes the total leverage that a pitcher can consume within those constraints. It’s not enough to pick the right person for the job when the job is hard; it’s also necessary to pick the right person for the job when the job is somewhat easier, so that the right person for the next hard job is available. Michael Lorenzen did the hard job, but he also did an easier one, and as a result, wasn’t available for the next hard job.

Let me begin by clarifying that this article is not conceived as an attempt to downplay Trout’s historic greatness. I am a Trout devotee, fully in awe of his talent and willing to debate with Baby Boomers about his worthiness of winning the AL MVP just about every season (Miguel Cabrera’s Triple Crown be damned!). However, there is one baseball player I worship above all the rest – Mickey Mantle.

As a child growing up with an undying love for baseball and reading, I devoured just about every baseball book I could. Player biographies, compendiums claiming they could list the top 100 (or even 1,001) ballplayers of all time, and wacky collections of random fun facts. My favorite of all of these was “The Mick,” Mantle’s autobiography. This book made me fall in love with his story, and I acquired an appreciation for him as one of the all-time greats beyond his stat line. And that, folks, is why Mike Trout will never be Mickey Mantle – the overwhelming power of narrative vaults Mantle to a level beyond just about any player this side of Babe Ruth. In his book The Truth About Stories, Thomas King tells us that “the truth about stories is that’s all we are.” Our entire humanity is shaped by stories. No collection of numbers or in-depth analysis of WAR, launch angles, and exit velocities can combat the enormous influence of stories. Trout, legendary talent and all, will (barring extraordinary future events) never have a story that lives up to Mantle’s.

If you’re unfamiliar with Mickey Mantle’s story, maybe it’s been a while since you’ve listened to Bob Costas pontificate. Let me refresh you. Here’s really all that matters; the life-blood of Mantle’s legend. He was supposed to be even better than he actually was! Here’s a photo of the injury that derailed everything.

You can just see DiMaggio standing over him, lamenting the fact Mantle will only accumulate a mere 111 career WAR from this moment onward. This career-destroying injury caused Mantle to hit only 523 home runs from this moment onward.

Let’s talk about home runs for a second. Mantle’s mammoth power from both sides of the plate is the skill he is best known for. The term “tape-measure home run” was coined because of his power, in case you didn’t know. Mike Trout hits some tape-measure home runs himself. Here’s the longest home run of Trout’s career, a 489-foot blast at Kaufman Stadium.

Impressive, no doubt. That’s a long way to hit a baseball. We can see that from the video. In fact, the video provides irrefutable evidence of the event occurring, with ESPN’s Home Run Tracker giving us accurate statistical information. Had the home run been hit in the Statcast era we’d have an even more detailed account of the home run.

Mantle’s home runs are historic, legendary, and record-setting. We know this because we’re told this. We have no Statcast data to back this up, but the stories we’re told are compelling. Purportedly, Mantle once hit a ball 734 feet. Well, he would have, had the ball not hit the façade atop Yankee Stadium while still reaching the apex of its flight. Don’t believe it? Check out the definitive list of Mantle home runs.

It’s unbelievable, right? With no Statcast or ESPN mathematicians to aid us, all we have are eyewitness accounts and crude trigonometric measures to give us a less-than-definitive representation of reality. What matters isn’t how far the ball was actually hit. What matters is that there is a story surrounding the home run. In the same way his early-career knee injury begs the question of “What if?”, so too does this home run leave room for imagination. The façade acts as the sprinkler head, an impediment between what was and what would have been.

What could Mike Trout be? We’ll have to wait for time to answer that question. In any given moment, though, we’ll never have to question what he is. We’ll know the exact speed the ball comes off his bat, the exact distance of his home runs, and the precise amount of ground he covers as he tracks down fly balls. That’s the real crux of the issue – we know too much. Even though Trout plays on the West Coast and arguably doesn’t receive enough recognition or publicity, we have all the information we need and more. We can quantify his achievements in a way we never could, except retroactively, with Mantle.

Mickey Mantle’s stellar career was plagued by knee injuries, alcoholism, and pesky stadium facades. Mike Trout’s career will always be followed by WAR-calculating analysts, 24/7 media coverage, and the omnipresence of Statcast. Subjectivity will always be more compelling than objectivity. It’s human nature. The truth about stories is that’s all we are, and the truth is that Mike Trout will never be Mickey Mantle.

After reading this post on predicting batted-ball type based on exit velocity and launch angle, I thought it might be neat to see how it could be applied to reducing the effects of luck and defense on wOBA.

The idea is that wOBA correctly values outcomes; however, wOBA implicitly assumes the batter has total control over the outcome of a batted-ball event based on his input to the system. I tried awarding the batter value based on the expected value of balls hit with similar launch angles and exit velocities. Instead of treating the outcomes as events, I assigned them a value based on the wOBA weights used for the season of interest.

I used the random forest classifier from the referenced post but looked at outcomes relevant to the wOBA formula. The probabilities of each outcome based on the exit velocity and launch angle of the batted-ball events are multiplied by the wOBA weights to give an expected value for the batted-ball event.

The classifier was trained on all batted-ball events from the 2015 season. The model accurately classified only 70% of the 2016 batted-ball events, so there may be a problem with over-fitting. The use of probabilities rather than plain classification should help to reduce error.

The plot below is a graphical representation of the classifier. This shows what the classifier believes to be the most likely outcome for different levels of EV and LA. When using the individual probabilities, the model is more smooth.

I compared the number of at-bats for a player to regress to their season-long mean for this value metric with the number of at-bats for the player to regress to their season-long mean for the corresponding section of their wOBA. It should be noted I only counted at-bats where the batter put the ball in play.

It looks like it is capturing some information about the batter that is lost by considering only the true outcome of the batted-ball event. It takes fewer at-bats for the error to stabilize.

Jeff Samardzija is incredibly frustrating at times.  For the first few months of 2016, Giants fans saw a pitcher who would more than earn the five-year, $90-million contract he had signed in the offseason.  In April and May, Samardzija posted FIPs of 3.67 and 2.45, as well 11.9% and 19.1% K-BB rates.  Those numbers are pretty worthwhile considering Samardzija has forged himself into a workhorse, averaging over 200 IP over the past four seasons.  The Giants would be plenty happy with that for a full season.  All seemed well in Giants land.  The free agents were proving their worth, Madison Bumgarner’s greatest concern was with his own hitting (that may always be true), and Buster Posey was healthy.  The even-year sorcery seemed to be working.

June and July came around, though, and Samardzija saw himself regress into what looked like the 2015 version of himself.  In June and July Samardzija posted FIPs of 7.09 and 5.06.  Samardzija was giving up homers at an alarming pace and he was desperately struggling to strike people out.  Oddly enough, Samardzija was drastically altering his pitch mix in the middle of the year.

Holy cow.

That looks experimental more than anything else.  For Samardzija to maintain his level of performance even in his good months is pretty solid given such drastic changes in pitch mixes.

For reference, here is Samardzija’s FIP throughout the course of last year.

You can see the success I mentioned earlier before June and July came around, but Samardzija also set out on a strong end to the season, posting a 3.67 FIP in August and a 2.38 FIP in September/October to somehow bring his FIP below the league average.  That final stretch also saw Samardzija posting a 21.8 K-BB% as well, maintaining a similar walk rate he posted all season while striking out 28.6% of batters.

Staring through the bevy of pitches Samardzija featured through the season, you can see where he was getting to in the end.  He almost entirely ditched his cutter and found a balance between his four-seam and two-seam fastballs.  The curveball usage held steady, the slider usage went down, and the splitter continued to emerge as a favorite.  The splitter usage has appeared to come about as Samardzija’s neutralizer towards lefties, and it has worked well.  Lefties have given Samardzija trouble for his whole career and the near-60-point difference in wOBA versus lefties last year is fairly alarming (.331 vs .276), so an offspeed pitch that moves away from lefties is crucial.

That splitter itself is fairly similar in movement to Masahiro Tanaka’s.

Samardzija: -6.7 x, 3.9 z

Tanaka: -6.7 x, 3.3 z

Should Samardzija use the splitter versus lefties as much as Tanaka does (nearly 30%!) and locate it as Tanaka does (low and away from lefties), it should be effective, given his SwStr% with the pitch throughout his career (19.5%).

Here is Samardzija in his last tune-up before the season.

(Skip to 0:13 for the nasty nasty.)

In those final two months last year, Samardzija was able to continually do better against righties while limiting lefties to a somewhat manageable .410 SLG.  Should Samardzija maintain a similar pitch mix, he would look more like his four-win 2014 campaign.  Pitching isn’t that simple, but he’s making his way back to something that had worked quite well for him in the past.

The 2016 Giants season became all about the monstrous second-half collapse, but hidden in there was a bit of a Jeff Samardzija resurgence.  In 2017, Samardzija will almost assuredly be worth his salary in durability alone.  But if he can continue to utilize his splitter as he had toward the end of 2016, I would expect him to outperform his projections (Steamer 3.84 ERA 3.78 FIP 4.09 xFIP) and deliver a performance more in line with his 2014 season.  The Giants rotation already runs deep, but they could be looking at one of the most durable and effective groups of front-line starters in the game.

First game of the season for the Giants, and the bullpen’s falter in the 8th and 9th inning is terrifying. The fear comes from the reminiscence of the ghost of 2016. The addition of Mark Melancon, and departure of the core of the Giants pen, seemed to be the remedy for the expulsion of this ghost, but opening day seemed to tell a different tale.

The new setup man in the 8th inning, Derek Law, came in to relieve Madison Bumgarner, who took the Giants into the 8th with a 4-3 lead that he pretty well mustered up all alone. Law gave up back-to-back singles, before a meeting was called at the mound. Law gave up another single to Paul Goldschmidt, surrendering a run, and the lead. Ty Blach was summoned from the pen for a lefty-lefty match up against Jake Lamb, and he got him to ground out into a double play, and Bruce Bochy then went for his righty-righty matchup with Hunter Strickland against Yasmany Thomas, which ended up in a ground out to get out of the inning with a tie ball game.

My argument is that Bochy’s uncertainty on how he is going to handle his pen is, perhaps, one of the reasons for this supposed curse. Before the season started, the underlining story was that the pen would be fixed by the certainty of roles, as Melancon was the sure closer and this definitive role was going to bring stability to the pen that was not there last season. However, the setup man in the 8th gets banged up for three hits in a row, and Bochy immediately cuts the cord for his matchup ideals. These matchups end up working, and they get out of the inning relatively unscathed. However, it seems that this lack of trust for his relievers to get out of trouble may be one of the reasons the bullpen struggles when the game is tight in the late stages.

Let’s compare to the three other teams who had to pitch in tight situations in the closing stages that same day.

Their opponent, the D-backs:

J.J. Hoover comes in at the top of the 8th with his club down by one. With one out, he walks Buster Posey and allows Brandon Crawford to single. Torey Lovullo allows Hoover to get himself out of danger to end the 8th.

Fernando Rodney comes into the 9th with the game tied, and immediately gets hit for a triple. He gets a sac fly for his first out, but allows a run, to give the Giants a lead. He then allows a single, throws a wild pitch, walks Brandon Belt, throws a wild pitch, and walks Hunter Pence. Instead of pulling him after a mound visit, Lovullo allows Rodney to work out of his trouble, and Rodney gets a fly out and a ground out to end the inning.

Cubs:

Bottom of the 8th, down by a run, Joe Maddon uses Pedro Strop. First hitter he sees, he walks, then a pop-up, and then he allows a two-run HR. He then walks his next batter, but finally works his way out of the inning with back-to-back ground outs. Maddon uses Mike Montgomery in the bottom of the 9th of a tied game. He allows a one-out double, and Maddon comes out to talk him through the inning. He intentionally walks Yadier Molina to set up a possible inning-ending double play. He gets a K but then walks Kolten Wong, and is eventually led to his loss by a line drive to left field by Randal Grichuk.

Cardinals:

After doing a good job getting the final two outs of the 8th, Seung Hwan Oh was asked to close out the top of the ninth. He hits Ben Zobrist with a pitch, Ks Addison Russell, then is hit for a single and hit for a three-run HR, but then closes out the inning with a K and a pop-up.

You could argue here that the D-Backs and Cards just won because of their scoring output in the 9th, and that the Cubs had the same fate as the Giants. However, what I am trying to argue is that the short leash that Bochy demonstrated in the 8th is an outlier to the other three managers, and perhaps, may be an element that has been driving the curse of the bullpen.

Bochy’s tactics get really twisted as he allows Melancon the long leash to try and work his way out of danger in the 9th. Presumably because Melancon is the undisputed closer, and he had two outs in the inning. However, it seems like the stability of the bullpen becomes unraveled as soon as the short leash is initiated in the 8th.

If Bochy believes the curse was created from the instability of not having a definitive closer, than perhaps it is also the instability of definitive roles in the pen. If he believed that Law deserves the 8th inning setup role over Strickland, then he should stick to his guns and let Law pitch out of the 8th inning. (Still not sure how Matt Cain got the fifth spot over Blach.) If he lets up and wants to shuffle up the roles for the next game, then so be it, but the shift from short leash to long leash, concrete roles to matchup roles, all seem to be unbalancing to the pen.

Nothing is more evident of this than the series against the Cubs last October. Game 3, Bochy lets Sergio Romo finish up his work in the 9th, but not before Romo had given up two runs and allowed the Cubs to tie. The Giants would end up winning this game. Game 4, on the other hand…up 5-2 in the 9th, Bochy uses Law, who immediately allows a single and is pulled for Javier Lopez. Lopez walks Anthony Rizzo and is pulled for Romo. Romo allows a double and is pulled for Will Smith. Smith allows a single, and then gets the first out on Jason Heyward’s bunt. He is then pulled for Strickland, who allows a single, but then ends the inning with a double play. The Giants end their season with a monumental bullpen collapse in the 9th inning.

This short-leash/ r-r l-l matchup tactic that Bochy sometimes uses, and sometimes does not, seems to have a role in the haunting of this Giants pen. While last year he never had the luxury of that star closer, and definitely does not have the likings of a Clippard-Betances-Chapman bullpen, I think Bochy does fare better when he allows his bullpen to settle into roles with a margin of error. Moreover, the Giants have a great bullpen of Strickland-Law-Melancon and supporting cast. However, the bullpen probably fares better when the question mark of that order disappears and the setup men have the chance to play out their roles.

Hell, we are one game into the season and do not know if the bullpen is still cursed, but if it is, perhaps the curse is caused by the handling of the pen, and not the skill within it.

The analytics revolution in sports has led to profound changes in the way in which sports organizations think about their teams, players play the game, and fans consume the on-field product. Perhaps the best-known heuristic in sports analytics is sample size — the number of observations necessary to make a reliable conclusion about some phenomenon. Everyone has a buddy who loves to make sweeping generalizations about stud prospects, always hedging his bets when the debate heats up: “Well, we don’t have enough sample size, so we just don’t know yet.”

Unfortunately for your buddy, sample size doesn’t tell the whole story. A large sample is a nice thing to have when we’re conducting research in a sterile lab, but in real-life settings like sports teams, willing research participants certainly aren’t always in abundant supply. Regardless of the number of available data points, teams need to make decisions. Shrugging about a prospect’s performance, or a newly cobbled together pitching staff, is certainly not going to help the bottom line, either in terms of wins or dollar signs.

So the question becomes: How do organizations answer pressing questions when they either a) don’t have an adequate sample size, or b) haven’t collected any data? Fortunately, we can use research methods from social science to get a pretty damn good idea about something — even in the absence of the all-powerful sample size.

Qualitative Data
Let’s say you’re a baseball scout for the Yankees watching a young college prospect from the stands. You take copious notes about the player’s poise, physical stature, his hitting, fielding ability, and running abilities, as well as his throwing arm power. For instance, you might write things like, “good approach to hitting” and “lacks pure run/throw tool.”

All of these rich descriptions of this player are qualitative data. This observational data from one game of this college player is a sample size of 1, but you’ve got a helluva lot of data. You could look for themes that consistently emerge in your notes, creating an in-depth profile of the prospect; you could even standardize your observations on a scale from 20-80. Your notes help build a full story about the player’s profile, and the Yanks like the level of depth you bring to scouting.

Mixed-Methodology
You’ve worked as a scout for a few years, and the Yankees decide to bring you into their analytics department. It’s the end of the 2011 season, and one of your top prospects, Jesus Montero, just raked (.328/.406/.590, in 69 PAs) in the final month of the season. The GM of the Yankees, Brian Cashman, knocks on your door and says that they’re considering trading him. What do you say?

You compile all of Montero’s quantitative stats from the last month of the season and the minors, as well as any qualitative scouting reports on him. Good job. You’ve mixed quantitative and qualitative data to provide a richer story given a small sample of only 69 PAs. You’ve also reached the holy grail of social science research, triangulation, by which you examined the phenomenon from a different angle and, bingo, arrived at the same conclusion that your preliminary performance metrics gave you. Montero is a bum. Trade him, Brian.

Resampling Techniques
It’s four years later and Cashman knocks on your door again (he’s polite, so he waits for you to say, “come in”). It’s early October and you’ve just lost to the Houston Astros in a one-game playoff. Cashman asks you about one of the September call-ups, Rob Refsnyder, who Cashman thinks is “pretty impressive.” You combine Refsnyder’s September stats (.302/.348/.512, in 46 PAs), minor league stats, and scouting reports, but the data don’t point to a consistent conclusion. You’re not satisfied.

A fancy statistical method that might help in this instance is called bootstrapping; it works by resampling Refsnyder’s small 46 PA sample size over and over again, replacing the numbers back into the pool every time you draw another sample. The technique allows you to artificially inflate your sample size with the numbers that you already have. You can redo his sample of 46 PAs 1,000, 10,000, even 100,000 times, seeing each time how he would perform. Based on your bootstrapped estimates, you feel like Refsnyder’s numbers from last year are a bit inflated, but that he’d fit nicely as a future utility guy.

Non-Parametrics
Cashman, who’s still in your office, now wants to know about two pitching prospects who were also called up in the 2015 class: James Pazos (5 IP, 0 ER, 3 H, 3BB, 5.4 K/9, 1.20 WHIP) and Caleb Cotham (9.2 IP, 7 ER, 14 H, 1BB, 10.2 K/9, 1.56 WHIP). If the team can only keep on of these pitchers, who should we keep? Who is better?

Normally you’d use a t-test to make comparisons between the two pitchers, but with such a small sample of innings for each guy, the conclusions wouldn’t be reliable. Instead, you decide to use a Mann-Whitney U test, which is basically the same thing as a t-test, adjusted for small samples. In fact, there’s a whole litany of statistical tests that are adept at handling small sample sizes: Wilcox’s t, Fisher’s exact, Chi-square, Kendal’s tau, and McNemar. You conclude that Pazos is slightly better, and that Cotham might be better suited for the bullpen. Cashman holds on to Pazos and deals Cotham to the Reds in the trade that brings over Aroldis Chapman to the Yankees. You pat yourself on the back.

Questions Need Answering
Having an adequate sample size brings confidence to many statistical conclusions, but it is certainly not a binary prerequisite for analyses. It’s easy for your buddy to watch his hindsight bias autocorrect for his previous wait-and-see approach, but organizations need to answer questions accurately. As amateur analysts and spectators, let’s change the lexicon by changing our methods.

Why are we so weird?

We don’t have 13th floors in hotels, walk under ladders, or pick up coins facing tails-up because all of these things are bad luck. People knock on wood when they talk about the future. They say “God bless you” if you sneeze, for fear of your soul escaping.

And as if those habits weren’t odd enough, ballplayers and baseball go and take superstition to a whole new level of silly and agitating.

The worst is the concept of the jinx during a no-hitter. Under what circumstances does uttering some passing phrase about a pitcher’s no-hitter suddenly doom it? Even if it’s deliberate, how does that change a guy’s ability to paint the black or shoot a blooper? Maybe it’s some cosmic understanding that goes over the head of simpler folks. But baseball is a game that is constantly relying more and more heavily on numbers, odds, and percentages. A no-hitter is one thing we can accurately acknowledge in the moment and without in-depth analysis. Doing so is no foible.

A pitcher’s team not talking to him during a no-hitter is just fine, though. It makes out a single game as something special, and how often do we get to do that during the regular season? That pitcher is on a mission that has been accomplished only 252 times since 1901. Currently, there are nearly 2,500 games in a single season. If a guy’s doing something that’s only been done a fraction of a single percentage in all the games in modern history, there’s no reason to goof with him like it’s just another day at the park. To that point, it hasn’t been.

Other superstitions are ones that have become prominent because of the volume at which they occur. Guys skip over the chalk at the start and finish of every inning on the way out of and to the dugout. It’s okay to think, “But what would happen if they did hit the line just once? No one is going to get hurt. It isn’t going to break a teammate’s mother’s back like stepping on a crack.” Let’s remember, though: the inning is over. Commercials are about to start. That silly moment is an easy one to tune out, so we’d be best off doing just that when we find ourselves fixated on it.

But when the game is back, and a player’s getting ready to pitch or step into the box, we’re paying attention. And we notice those ridiculous, idiosyncratic tics that turn into superstition which so many guys maintain. They work them into their mechanics and if they don’t perform them they’re thrown off. I’m looking at you, Matt Garza. Your little glove twitch has been the visual equivalent to a throw-up burp. It’s unpleasant and people might take a drink of the nearest beverage just to forget it.

Though he’s retired, Nomar Garciaparra remains the king of batting-glove love. Each time he stepped to the plate he might as well have played pat-a-cake with himself. It’s nothing compared to Moises Alou, though, who refused to wear batting gloves and would pee on his hands to toughen them up. Gross.

In all this strangeness, through all this exercised peculiarity, there might be some logic, even though the very definition of superstition tells us there isn’t.

In an episode of Fresh Air titled “Habits: How They Form And How To Break Them,” we learn about something called the habit loop from Charles Duhigg, author of The Power of Habit: Why We Do What We Do in Life and Business. There are three steps to it: a cue, a routine, and a reward. The cue enables the brain to let a behavior happen, while the routine is the actual action, and the reward is the brain enjoying it all and making it easier to remember.

That process becomes automated rather quickly. Scientists attribute it to the basal ganglia, which “plays a key role in the development of emotions, memories and pattern recognition.” You might realize how none of this speaks to the actual decision of players to do quirky things like skip over foul lines or fiddle with their equipment a certain way. That’s because the part of our brain that makes decisions — the prefrontal cortex — checks out once a behavior becomes automatic. It appears that once someone starts a habit, in many cases they’re not actually choosing to continue it.

Habits do provide comfort, though. And habits held in the belief of good fortune are why we get silly baseball superstitions that we can laugh at or hate. Whether they’re rare or regular occurrences, they’re one more way the game gives back to us.